The present invention relates generally to techniques for organizing images of defects found on integrated circuit devices. Additionally, it relates to techniques for searching through such defect images for one or more images that have a similar appearance to a target defect image and analyzing the found images to help determine a cause of the target defect.
Semiconductor defects may include structural flaws, residual process material and other surface contamination which occur during the production of semiconductor wafers. Defects are typically detected by a class of instruments called inspection tools. Such instruments automatically scan wafer surfaces and detect, and record the location of optical anomalies using a variety of techniques. This information, or “defect map,” is stored in a computer file and sent to a defect review station.
Using the defect map to locate each defect, a human operator observes each defect under a microscope and classifies each defect according to class (e.g., particle, pit, scratch, or contaminant). Information gained from this process is used to correct the source of defects, and thereby improve the efficiency and yield of the semiconductor production process. Problems with this classification method include the technician's subjectivity in identifying the defect class, and the fatigue associated with the highly repetitive task of observing and classifying these defects.
Methods of automatically classifying defects, collectively known as Automatic Defect Classification or “ADC,” have been developed to overcome the disadvantages of manual defect classification. In conventional ADC, review stations are automated to load a wafer that has been mapped for defect location by a defect scanner. Once the mapped wafer is loaded, the review station:
1. positions the wafer to image the site of a defect, as indicated by the defect map;
2. focuses on the site of the defect;
3. captures a digital image of the site;
4. processes and analyzes the captured image of the site to locate the defect within the image; and
5. further analyzes the data to classify the defect.
The above process is repeated for each defect (or a predetermined subset of defects) on the wafer. The wafer is then unloaded and the process is repeated for another wafer. By eliminating a fatiguing and highly repetitive task, such automated review stations reduce labor costs and provide improved consistency and accuracy over human operators.
Accordingly, an ADC system uses image processing techniques to first detect the defect and then to classify the defect according to the defect's physical characteristics and background geometry. Comparing these physical characteristics to the physical characteristics of pre-classified defects in a knowledge database (KDB) permits automated defect classification. While this system reduces technician fatigue and increases the number of defects that can be classified per unit time, there is presently a problem with such KDB programs providing an accurate classification.
Generating a KDB for classifying objects typically utilizes direct coding methods, whereby an operator subjectively selects what are thought to be typical defects and enters the defect images and predetermined classification codes into a detection system to generate a knowledge database that is later used by an ADC system for classifying unknown defects.
A knowledge database generated by this method is subject to inaccuracy since human variability, introduced in the initial subjective identification of the defect type, is included in the KDB. Moreover, present database generating methods, including direct coding, are time consuming and require a significant amount of highly trained, manpower resources.
Once the defect images are classified, a search may be performed on the defect images of a particular class to look for one or more images which have been previously assigned to the same class. However, if the underlying classes are inaccurately defined, the search will produce inaccurate results. The search will also not be able to locate defects which have not been previously classified. For example, images that should be classified in the class which is searched may be left out of the search. A conventional image search typically includes comparing the target image to each image within the same class as the target image. Since each class may have thousands of defect images, this search can take a significant amount of time.
Accordingly, there is a need for improved mechanisms for organizing defect images, searching through such images, and/or analyzing the results from such a search.